Comparison Study of Sentiment Analysis of Tweets using Various Machine Learning Algorithms

Kanakaraddi, Suvarna G.; Chikaraddi, Ashok K.; Gull, Karuna C.; Hiremath, P. S.

doi:10.1109/icict48043.2020.9112546

Cited by 21 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Kanakaraddi et al [6] have introduced an analysis of diverse supervised machine learning methods for opinion mining such as SVM, random forest, max entropy, and naive bayes. Amongst all these methods, the SVM gives a better classification rate equal to 79.90%.…”

Section: Literature Reviewsmentioning

confidence: 99%

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Es-sabery

Garmani

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applied several pre-processing techniques. Secondly, we have used the fastText method for vectoring the analysed tweets. Thirdly, we have implemented the CNN for extracting and selecting the pertinent features from the tweets. Fourthly, we have fuzzified the CNN output using the Gaussian Fuzzification (GF) method for coping with vague data. Then we have applied fuzziness C4.5 for creating the fuzziness rules. Finally, we have used the General Fuzziness Reasoning (GFR) approach for classifying the new tweets. In summary, our method integrates the advantages of CNN and C4.5 techniques and overcomes the shortcomings of ambiguous data in the tweets using FRBS, which is consists of three-phase: fuzzification phase using GF, inference mechanism using fuzziness C4.5, and defuzzification phase using GFR. Also, to give our approach the ability to deal with the massive data, we have implemented it on the Hadoop framework of five computers. The experiential findings confirmed that our model operates excellently compared to other chosen models form the literature.

show abstract

Section: Literature Reviewsmentioning

confidence: 99%

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Es-sabery

Garmani

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…For a data set consisting of features set and labels set, an SVM classifier builds a model to predict the classes for the new examples. It assigns a new case or data points to one of the categories [19]. Algorithm: a) Define an optimal hyperplane b) Extend step I for nonlinearly separable problems c) Map data to high dimensional space where it is easy to classify with linear decision surfaces.…”

Section: Naïve Bayes' Multinomial Classification Is a Modified Version Of A Text-related Algorithm Mainly Used For Classification Taking mentioning

confidence: 99%

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

Habibi

Priadana

Ma’arif

2021

IJID

View full text Add to dashboard Cite

The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.

show abstract

“…For the training point set ( , ), where is the feature vector is the class. To determine the maximum limit of the hyperplane dividing the points by = 1 and = 1 [18]. To find the hyperplane Equation ( 4) is used.…”

Section: 5mentioning

confidence: 99%

Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

Rahmawati

Habibi

2020

IJID

View full text Add to dashboard Cite

Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency.

show abstract

Comparison Study of Sentiment Analysis of Tweets using Various Machine Learning Algorithms

Cited by 21 publications

References 9 publications

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

Contact Info

Product

Resources

About